1. Neuroscience
Download icon

Pre-stimulus phase and amplitude regulation of phase-locked responses is maximized in the critical state

  1. Arthur-Ervin Avramiea
  2. Richard Hardstone
  3. Jan-Matthis Lueckmann
  4. Jan Bim
  5. Huib D Mansvelder
  6. Klaus Linkenkaer-Hansen  Is a corresponding author
  1. Vrije Universiteit Amsterdam, Netherlands
  2. Neuroscience Institute, New York University School of Medicine, United States
  3. Technical University of Munich, Germany
  4. Czech Technical University in Prague, Czech Republic
Research Article
  • Cited 4
  • Views 1,633
  • Annotations
Cite this article as: eLife 2020;9:e53016 doi: 10.7554/eLife.53016

Abstract

Understanding why identical stimuli give differing neuronal responses and percepts is a central challenge in research on attention and consciousness. Ongoing oscillations reflect functional states that bias processing of incoming signals through amplitude and phase. It is not known, however, whether the effect of phase or amplitude on stimulus processing depends on the long-term global dynamics of the networks generating the oscillations. Here, we show, using a computational model, that the ability of networks to regulate stimulus response based on pre-stimulus activity requires near-critical dynamics—a dynamical state that emerges from networks with balanced excitation and inhibition, and that is characterized by scale-free fluctuations. We also find that networks exhibiting critical oscillations produce differing responses to the largest range of stimulus intensities. Thus, the brain may bring its dynamics close to the critical state whenever such network versatility is required.

Data availability

Source code required to run all simulations, as well as datasets and scripts required to generate all figures presented here, are available on figshare.

The following data sets were generated

Article and author information

Author details

  1. Arthur-Ervin Avramiea

    Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0826-8269
  2. Richard Hardstone

    Perception and Brain Dynamics Laboratory, Departments of Neurology, Neuroscience and Physiology, and Radiology, Neuroscience Institute, New York University School of Medicine, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7502-9145
  3. Jan-Matthis Lueckmann

    Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Jan Bim

    Computer Science, Czech Technical University in Prague, Prague, Czech Republic
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2780-5610
  5. Huib D Mansvelder

    Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1365-5340
  6. Klaus Linkenkaer-Hansen

    Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, Netherlands
    For correspondence
    k.linkenkaerhansen@vu.nl
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2140-9780

Funding

Netherlands Organization for Scientific Research (612.001.123)

  • Richard Hardstone
  • Klaus Linkenkaer-Hansen

Netherlands Organization for Scientific Research (406.15.256)

  • Arthur-Ervin Avramiea
  • Klaus Linkenkaer-Hansen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Floris P de Lange, Radboud University, Netherlands

Publication history

  1. Received: October 24, 2019
  2. Accepted: April 20, 2020
  3. Accepted Manuscript published: April 23, 2020 (version 1)
  4. Accepted Manuscript updated: April 27, 2020 (version 2)
  5. Version of Record published: May 12, 2020 (version 3)

Copyright

© 2020, Avramiea et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,633
    Page views
  • 204
    Downloads
  • 4
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Neuroscience
    Justin W Kenney et al.
    Tools and Resources Updated

    Zebrafish have made significant contributions to our understanding of the vertebrate brain and the neural basis of behavior, earning a place as one of the most widely used model organisms in neuroscience. Their appeal arises from the marriage of low cost, early life transparency, and ease of genetic manipulation with a behavioral repertoire that becomes more sophisticated as animals transition from larvae to adults. To further enhance the use of adult zebrafish, we created the first fully segmented three-dimensional digital adult zebrafish brain atlas (AZBA). AZBA was built by combining tissue clearing, light-sheet fluorescence microscopy, and three-dimensional image registration of nuclear and antibody stains. These images were used to guide segmentation of the atlas into over 200 neuroanatomical regions comprising the entirety of the adult zebrafish brain. As an open source, online (azba.wayne.edu), updatable digital resource, AZBA will significantly enhance the use of adult zebrafish in furthering our understanding of vertebrate brain function in both health and disease.

    1. Neuroscience
    Cédric Foucault, Florent Meyniel
    Research Article

    From decision making to perception to language, predicting what is coming next is crucial. It is also challenging in stochastic, changing, and structured environments; yet the brain makes accurate predictions in many situations. What computational architecture could enable this feat? Bayesian inference makes optimal predictions but is prohibitively difficult to compute. Here, we show that a specific recurrent neural network architecture enables simple and accurate solutions in several environments. This architecture relies on three mechanisms: gating, lateral connections, and recurrent weight training. Like the optimal solution and the human brain, such networks develop internal representations of their changing environment (including estimates of the environment's latent variables and the precision of these estimates), leverage multiple levels of latent structure, and adapt their effective learning rate to changes without changing their connection weights. Being ubiquitous in the brain, gated recurrence could therefore serve as a generic building block to predict in real-life environments.